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Review Articles

Vol. 13 No. sp9 (2026): Recent Advances in Agriculture

Dynamic thermal imaging for crop water stress detection across phenological stages: Enhancing irrigation scheduling precision

DOI
https://doi.org/10.14719/pst.17728
Submitted
28 August 2026
Published
21-10-2026

Abstract

Thermal remote sensing and derived indices such as the crop water stress index (CWSI) can assess crop water status and support precision irrigation. However, most operational and artificial intelligence (AI)-based approaches rely on fixed, season-long thresholds to interpret canopy–air temperature differences and often overlook the influence of crop phenology. This review synthesises evidence from grapevine, wheat, maize, potato and several other crops on how developmental stage affects the non-water-stressed baseline (NWSB), CWSI thresholds and irrigation-scheduling algorithms. It covers CWSI theory and estimation practices; crop-specific, phenology-dependent behaviour of baselines and thresholds; a comparison of threshold-based, machine learning, Internet of Things (IoT), digital twin and Food and Agriculture Organization (FAO)-56 approaches with respect to crop phenology; and a conceptual framework for phenology-aware scheduling. Across the reviewed crops, baseline behaviour generally varied systematically across the growing season, the relationship between CWSI and crop water status generally strengthened after canopy closure and CWSI measured during reproductive stages was generally most closely related to yield. A quantitative audit of 56 AI-, thermal-imaging-, or CWSI-based studies found that 78.6 % either did not explicitly incorporate crop phenology or relied on day of year as a temporal proxy, whereas only 5.4 % explicitly encoded phenological stage. The proposed framework considers phenology explicitly in baseline estimation, threshold setting and data fusion or machine learning for irrigation scheduling; the framework will require multi-year, multi-site field validation before operational use. Future research should prioritise standardised phenology definitions and long-term, multi-location studies to establish and validate stage-specific parameters.

References

  1. 1. Awais M, Li W, Cheema MJM, Zaman QU, Shaheen A, Aslam B, et al. UAV-based remote sensing in plant stress imagine using high-resolution thermal sensor for digital agriculture practices: a meta-review. Int J Environ Sci Technol.2023;20(1):1135–52. https://doi.org/10.1007/s13762-021-03801-5
  2. 2. Ahmad U, Alvino A, Marino S. A review of crop water stress assessment using remote sensing. Remote Sens. 2021;13(20):4155. https://doi.org/10.3390/rs13204155
  3. 3. Pineda M, Barón M, Pérez-Bueno ML. Thermal imaging for plant stress detection and phenotyping. Remote Sens. 2021;13(1):68. https://doi.org/10.3390/rs13010068
  4. 4. Sharma H, Sidhu H, Bhowmik A. Remote sensing using unmanned aerial vehicles for water stress detection: a review focusing on specialty crops. Drones. 2025;9(4):241. https://doi.org/10.3390/drones9040241
  5. 5. Sobejano-Paz V, Mikkelsen TN, Baum A, Mo X, Liu S, Köppl CJ, et al. Hyperspectral and thermal sensing of stomatal conductance, transpiration and photosynthesis for soybean and maize under drought. Remote Sens. 2020;12(19):3182. https://doi.org/10.3390/rs12193182
  6. 6. Zhang L, Zhang H, Zhu Q, Niu Y. Further investigating the performance of crop water stress index for maize from baseline fluctuation, effects of environmental factors and variation of critical value. Agric Water Manag. 2023;285:108349. https://doi.org/10.1016/j.agwat.2023.108349
  7. 7. Silveira JMDC, Góes JDA, Prado DFS, Pires RCDM, Chiorato AF. Baselines for calculating crop water stress index in bean cultivation. Int Arch Photogramm Remote Sens Spat Inf Sci. 2024;XLVIII-3-2024:501–6. https://doi.org/10.5194/isprs-archives-XLVIII-3-2024-501-2024
  8. 8. Gonzalez-Dugo V, Zarco-Tejada PJ. Assessing the impact of measurement errors in the calculation of CWSI for characterizing the water status of several crop species. Irrig Sci. 2024;42(3):431–43. https://doi.org/10.1007/s00271-022-00819-6
  9. 9. Mansoor S, Iqbal S, Popescu SM, Kim SL, Chung YS, Baek JH. Integration of smart sensors and IOT in precision agriculture: trends, challenges and future prospectives. Front Plant Sci. 2025;16:1587869. https://doi.org/10.3389/fpls.2025.1587869
  10. 10. Cho SB, Soleh HM, Choi JW, Hwang WH, Lee H, Cho YS, et al. Recent methods for evaluating crop water stress using AI techniques: a review. Sensors. 2024;24(19):6313. https://doi.org/10.3390/s24196313
  11. 11. Gautam D, Ostendorf B, Pagay V. Estimation of grapevine crop coefficient using a multispectral camera on an unmanned aerial vehicle. Remote Sens. 2021;13(13):2639. https://doi.org/10.3390/rs13132639
  12. 12. Tang Z, Jin Y, Alsina MM, McElrone AJ, Bambach N, Kustas WP. Vine water status mapping with multispectral UAV imagery and machine learning. Irrig Sci. 2022;40(4–5):715–30. https://doi.org/10.1007/s00271-022-00788-w
  13. 13. Pappalardo S, Consoli S, Longo-Minnolo G, Vanella D, Longo D, Guarrera S, et al. Performance evaluation of a low-cost thermal camera for citrus water status estimation. Agric Water Manag. 2023;288:108489. https://doi.org/10.1016/j.agwat.2023.108489
  14. 14. Idso SB, Jackson RD, Pinter PJ Jr, Reginato RJ, Hatfield JL. Normalizing the stress-degree-day parameter for environmental variability. Agric Meteorol. 1981;24:45–55. https://doi.org/10.1016/0002-1571(81)90032-7
  15. 15. Jackson RD, Idso SB, Reginato RJ, Pinter PJ Jr. Canopy temperature as a crop water stress indicator. Water Resour Res.1981;17(4):1133–8. https://doi.org/10.1029/WR017i004p01133
  16. 16. Katimbo A, Rudnick DR, DeJonge KC, Lo TH, Qiao X, Franz TE, et al. Crop water stress index computation approaches and their sensitivity to soil water dynamics. Agric Water Manag.2022;266:107575. https://doi.org/10.1016/j.agwat.2022.107575
  17. 17. Costa-Filho E, Chávez JL, Comas L. Determining maize water stress through a remote sensing-based surface energy balance approach. Irrig Sci. 2020;38(5–6):501–18. https://doi.org/10.1007/s00271-020-00668-1
  18. 18. Das S, Kaur S, Sharma V. Determination of threshold crop water stress index for sub-surface drip irrigated maize-wheat cropping sequence in semi-arid region of Punjab. Agric Water Manag.2024;301:108957. https://doi.org/10.1016/j.agwat.2024.108957
  19. 19. Jones HG. Use of infrared thermometry for estimation of stomatal conductance as a possible aid to irrigation scheduling. Agric For Meteorol. 1999;95(3):139–49. https://doi.org/10.1016/S0168-1923(99)00030-1
  20. 20. Moran MS, Clarke TR, Inoue Y, Vidal A. Estimating crop water deficit using the relation between surface-air temperature and spectral vegetation index. Remote Sens Environ. 1994;49(3):246–63. https://doi.org/10.1016/0034-4257(94)90020-5
  21. 21. Mokhtari A, Sadeghi M, Afrasiabian Y, Yu K. OPTRAM-ET: a novel approach to remote sensing of actual evapotranspiration applied to Sentinel-2 and Landsat-8 observations. Remote Sens Environ. 2023;286:113443. https://doi.org/10.1016/j.rse.2022.113443
  22. 22. Zhang L, Niu Y, Zhang H, Han W, Li G, Tang J, et al. Maize canopy temperature extracted from UAV thermal and RGB imagery and its application in water stress monitoring. Front Plant Sci. 2019;10:1270. https://doi.org/10.3389/fpls.2019.01270
  23. 23. Bian J, Zhang Z, Chen J, Chen H, Cui C, Li X, et al. Simplified evaluation of cotton water stress using high resolution unmanned aerial vehicle thermal imagery. Remote Sens. 2019;11(3):267. https://doi.org/10.3390/rs11030267
  24. 24. Guimarães N, Sousa JJ, Couto P, Bento A, Pádua L. Combining UAV-based multispectral and thermal infrared data with regression modeling and SHAP analysis for predicting stomatal conductance in almond orchards. Remote Sens. 2024;16(13):2467. https://doi.org/10.3390/rs16132467
  25. 25. Brewer K, Clulow A, Sibanda M, Gokool S, Odindi J, Mutanga O, et al. Estimation of maize foliar temperature and stomatal conductance as indicators of water stress based on optical and thermal imagery acquired using an unmanned aerial vehicle (UAV) platform. Drones. 2022;6(7):169. https://doi.org/10.3390/drones6070169
  26. 26. Peng J, Nieto H, Neumann Andersen M, Kørup K, Larsen R, Morel J, et al. Accurate estimates of land surface energy fluxes and irrigation requirements from UAV-based thermal and multispectral sensors. ISPRS J Photogramm Remote Sens.2023;198:238–54. https://doi.org/10.1016/j.isprsjprs.2023.03.009
  27. 27. Amogi BR, Pukrongta N, Khot LR, Sallato BV. Edge compute algorithm enabled localized crop physiology sensing system for apple (Malus domestica Borkh.) crop water stress monitoring. Comput Electron Agric. 2024;224:109137. https://doi.org/10.1016/j.compag.2024.109137
  28. 28. Mayanja IK, Yun H, Bailey BN. Automated calibration of stomatal conductance models from thermal imagery by leveraging synthetic images generated from Helios 3D biophysical model simulations. J Exp Bot. 2026;77(2):312–29. https://doi.org/10.1093/jxb/eraf420
  29. 29. Zhou Z, Majeed Y, Diverres Naranjo G, Gambacorta EMT. Assessment for crop water stress with infrared thermal imagery in precision agriculture: a review and future prospects for deep learning applications. Comput Electron Agric.2021;182:106019. https://doi.org/10.1016/j.compag.2021.106019
  30. 30. Messina G, Modica G. Applications of UAV thermal imagery in precision agriculture: state of the art and future research outlook. Remote Sens. 2020;12(9):1491. https://doi.org/10.3390/rs12091491
  31. 31. Ru C, Hu X, Wang W, Ran H, Song T, Guo Y. Evaluation of the crop water stress index as an indicator for the diagnosis of grapevine water deficiency in greenhouses. Horticulturae. 2020;6(4):86. https://doi.org/10.3390/horticulturae6040086
  32. 32. Park S, Ryu D, Fuentes S, Chung H, O’Connell M, Kim J. Dependence of CWSI-based plant water stress estimation with diurnal acquisition times in a nectarine orchard. Remote Sens. 2021;13(14):2775. https://doi.org/10.3390/rs13142775
  33. 33. Xue J anderson MC, Gao F, Hain C, Knipper KR, Yang Y, et al. Improving the spatiotemporal resolution of remotely sensed ET information for water management through Landsat, Sentinel-2, ECOSTRESS and VIIRS data fusion. Irrig Sci. 2022;40(4–5):609–34. https://doi.org/10.1007/s00271-022-00799-7
  34. 34. Guzinski R, Nieto H, Ramo Sánchez R, Sánchez JM, Jomaa I, Zitouna-Chebbi R, et al. Improving field-scale crop actual evapotranspiration monitoring with Sentinel-3, Sentinel-2 and Landsat data fusion. Int J Appl Earth Obs Geoinf. 2023;125:103587. https://doi.org/10.1016/j.jag.2023.103587
  35. 35. Xue J anderson MC, Gao F, Hain C, Yang Y, Knipper KR, et al. Mapping daily evapotranspiration at field scale using the Harmonized Landsat and Sentinel-2 dataset, with sharpened VIIRS as a Sentinel-2 thermal proxy. Remote Sens. 2021;13(17):3420. https://doi.org/10.3390/rs13173420
  36. 36. Atencia Payares LK, Gomez-del-Campo M, Tarquis AM, García M. Thermal imaging from UAS for estimating crop water status in a Merlot vineyard in semi-arid conditions. Irrig Sci. 2025;43(1):87–103. https://doi.org/10.1007/s00271-024-00955-1
  37. 37. Fuentes-Peñailillo F, Ortega-Farías S, Acevedo-Opazo C, Rivera M, Araya-Alman M. A smart crop water stress index-based IoT solution for precision irrigation of wine grape. Sensors. 2024;24(1):25. https://doi.org/10.3390/s24010025
  38. 38. Peeters A, Cohen Y, Bahat I, Ohana-Levi N, Goldshtein E, Netzer Y, et al. A spatial machine-learning model for predicting crop water stress index for precision irrigation of vineyards. Comput Electron Agric. 2024;227:109578. https://doi.org/10.1016/j.compag.2024.109578
  39. 39. Buunk T, Vélez S, Ariza-Sentís M, Valente J. Comparing nadir and oblique thermal imagery in UAV-based 3D crop water stress index applications for precision viticulture with LiDAR validation. Sensors. 2023;23(20):8625. https://doi.org/10.3390/s23208625
  40. 40. González-Vivar J, Demir S, Rodrigo-Comino J. Improving yield prediction and decision-making in Mediterranean vineyards using machine learning on multisource remote sensing and soil data. Air Soil Water Res. 2026;19:11786221261432379. https://doi.org/10.1177/11786221261432379
  41. 41. Flores-Olave J, Khan HA, Pérez I, Pacheco J, Cares J, Araya-Riquelme C, et al. Diurnal and phenological modulation of canopy temperature in wheat breeding under Mediterranean conditions. Plants. 2026;15(5):797. https://doi.org/10.3390/plants15050797
  42. 42. Liu H, Song W, Lv J, Gui R, Shi Y, Lu Y, et al. Precise drought threshold monitoring in winter wheat using the unmanned aerial vehicle thermal method. Remote Sens. 2024;16(4):710. https://doi.org/10.3390/rs16040710
  43. 43. Yadav A, Upreti H, Singhal GD. Crop water stress index and yield relationships for winter wheat (Triticum aestivum) crops grown under different drip and flood irrigated treatments. J Agron Crop Sci. 2024;210(6):e12775. https://doi.org/10.1111/jac.12775
  44. 44. Chandel NS, Rajwade YA, Dubey K, Chandel AK, Subeesh A, Tiwari MK. Water stress identification of winter wheat crop with state-of-the-art AI techniques and high-resolution thermal-RGB imagery. Plants. 2022;11(23):3344. https://doi.org/10.3390/plants11233344
  45. 45. Dwivedi AK, Ojha CSP, Singh VP. Crop water stress index for scheduling irrigation of wheat crop. J Irrig Drain Eng. 2024;150(3):04024006. https://doi.org/10.1061/JIDEDH.IRENG-10069
  46. 46. El-Hendawy S, Tahir MU, Al-Suhaibani N, Elsayed S, Elsherbiny O, Elsharawy H. Potential of thermal and RGB imaging combined with artificial neural networks for assessing salt tolerance of wheat genotypes grown in real-field conditions. Agronomy. 2024;14(7):1390. https://doi.org/10.3390/agronomy14071390
  47. 47. Xuan F, Liu H, Xue J, Li Y, Liu J, Huang X, et al. The novel triangular spectral indices for characterizing winter wheat drought. Int J Appl Earth Obs Geoinf. 2024;134:104151. https://doi.org/10.1016/j.jag.2024.104151
  48. 48. Liao Q, Gu S, Gao S, Du T, Kang S, Tong L, et al. Crop water stress index characterizes maize productivity under water and salt stress by using growth stage-specific non-water stress baselines. Field Crops Res. 2024;317:109544. https://doi.org/10.1016/j.fcr.2024.109544
  49. 49. Pradawet C, Khongdee N, Pansak W, Spreer W, Hilger T, Cadisch G. Thermal imaging for assessment of maize water stress and yield prediction under drought conditions. J Agron Crop Sci. 2023;209(1):56–70. https://doi.org/10.1111/jac.12582
  50. 50. Kapari M, Sibanda M, Magidi J, Mabhaudhi T, Nhamo L, Mpandeli S. Comparing machine learning algorithms for estimating the maize crop water stress index (CWSI) using UAV-acquired remotely sensed data in smallholder croplands. Drones. 2024;8(2):61. https://doi.org/10.3390/drones8020061
  51. 51. Kapari M, Sibanda M, Magidi J, Mabhaudhi T, Mpandeli S, Nhamo L. Assessment of the maize crop water stress index (CWSI) using drone-acquired data across different phenological stages. Drones. 2025;9(3):192. https://doi.org/10.3390/drones9030192
  52. 52. Bo L, Guan H, Mao X. Diagnosing crop water status based on canopy temperature as a function of film mulching and deficit irrigation. Field Crops Res. 2023;304:109154. https://doi.org/10.1016/j.fcr.2023.109154
  53. 53. Rinza J, Ramírez DA, Ninanya J, De Mendiburu F, García J, Quiroz R. Water saving using thermal imagery-based thresholds for timing irrigation in potatoes under drip and furrow irrigation systems. Agronomy. 2022;12(12):2921. https://doi.org/10.3390/agronomy12122921
  54. 54. Cucho-Padin G, Rinza J, Ninanya J, Loayza H, Quiroz R, Ramírez DA. Development of an open-source thermal image processing software for improving irrigation management in potato crops (Solanum tuberosum L.). Sensors. 2020;20(2):472. https://doi.org/10.3390/s20020472
  55. 55. Jaafar HH, Sujud LH. High resolution evapotranspiration from UAV multispectral thermal imagery: validation and comparison with EC, Landsat and fused S2-MODIS HSEB ET. Int J Appl Earth Obs Geoinf. 2025;136:104359. https://doi.org/10.1016/j.jag.2025.104359
  56. 56. Yang CY, Zhang YC, Hou YL. Assessing water status in rice plants in water-deficient environments using thermal imaging. Bot Stud. 2025;66(1):6. https://doi.org/10.1186/s40529-025-00452-4
  57. 57. Gu H, Mills C, Ritchie GL, Guo W. Water stress assessment of cotton cultivars using unmanned aerial system images. Remote Sens. 2024;16(14):2609. https://doi.org/10.3390/rs16142609
  58. 58. Lacerda LN, Snider JL, Cohen Y, Liakos V, Gobbo S, Vellidis G. Using UAV-based thermal imagery to detect crop water status variability in cotton. Smart Agric Technol. 2022;2:100029. https://doi.org/10.1016/j.atech.2021.100029
  59. 59. Jones SE, Ayanlade TT, Fallen B, Jubery TZ, Singh A, Ganapathysubramanian B, et al. Multi-sensor and multi-temporal high-throughput phenotyping for monitoring and early detection of water-limiting stress in soybean. Plant Phenome J. 2024;7(1):e70009. https://doi.org/10.1002/ppj2.70009
  60. 60. Ma S, Bi Y, Zhang Y, Wang K, Guo Y, Christie P. Thermal infrared imaging study of water status and growth of arbuscular mycorrhizal soybean (Glycine max) under drought stress. S Afr J Bot. 2022;146:58–65. https://doi.org/10.1016/j.sajb.2021.09.037
  61. 61. Bijanzadeh E, Moosavi SM, Bahadori F. Quantifying water stress of safflower (Carthamus tinctorius L.) cultivars by crop water stress index under different irrigation regimes. Heliyon. 2022;8(3):e09010. https://doi.org/10.1016/j.heliyon.2022.e09010
  62. 62. Gutiérrez-Gordillo S, García-Tejero IF, Durán Zuazo VH, García Escalera A, Ferrera Gil F, Amores-Agüera JJ, et al. Assessing the water-stress baselines by thermal imaging for irrigation management in almond plantations under water scarcity conditions. Water. 2020;12(5):1298. https://doi.org/10.3390/w12051298
  63. 63. Ramírez-Cuesta JM, Martínez-Gimeno MA, Badal E, Tasa M, Bonet L, Pérez-Pérez JG. UAV-based multispectral and thermal indexes for estimating crop water status and yield on super-high-density olive orchards under deficit irrigation conditions. Precis Agric. 2025;26(3):47. https://doi.org/10.1007/s11119-025-10240-6
  64. 64. Sapkota A, Roby M, Peddinti SR, Fulton A, Kisekka I. Comparative analysis of evapotranspiration (ET), crop water stress index (CWSI) and normalized difference vegetation index (NDVI) to delineate site-specific irrigation management zones in almond orchards. Sci Hortic. 2025;339:113860. https://doi.org/10.1016/j.scienta.2024.113860
  65. 65. Sánchez-Virosta A, Gómez-Candón D, Montoya F, Pérez-García Y, Jiménez V, Martínez-López JA, et al. Remote and proximal sensing assessment of water status and its correlation with yield on almond orchards in Southeast Spain. Precis Agric. 2025;26(6):92. https://doi.org/10.1007/s11119-025-10290-w
  66. 66. Peres DJ, Cancelliere A. Analysis of multi-spectral images acquired by UAVs to monitor water stress of citrus orchards in Sicily, Italy. In: World Environmental and Water Resources Congress 2021. Reston (VA): American Society of Civil Engineers; 2021. p. 270–8. https://doi.org/10.1061/9780784483466.025
  67. 67. Alordzinu KE, Li J, Lan Y, Appiah SA, Al Aasmi A, Wang H. Rapid estimation of crop water stress index on tomato growth. Sensors. 2021;21(15):5142. https://doi.org/10.3390/s21155142
  68. 68. Boyaci S, Kocięcka J, Atilgan A, Liberacki D, Rolbiecki R, Saltuk B, et al. Evaluation of crop water stress index (CWSI) for high tunnel greenhouse tomatoes under different irrigation levels. Atmosphere. 2024;15(2):205. https://doi.org/10.3390/atmos15020205
  69. 69. Aversano L, Bernardi ML, Cimitile M. Water stress classification using convolutional deep neural networks. J Univers Comput Sci. 2022;28(3):311–28. https://doi.org/10.3897/jucs.80733
  70. 70. Rojek L, Möller M, Richter M, Bischoff-Schaefer M, Hehl K. PLANTSENS: a rail-based multi-sensor imaging system for redundant water stress detection in greenhouses. Smart Agric Technol. 2023;4:100223. https://doi.org/10.1016/j.atech.2023.100223
  71. 71. Al-Najadi R, Al-Mulla Y, Al-Abri I, Al-Sadi AM. Effectiveness of drone-based thermal sensors in optimizing controlled environment agriculture performance under arid conditions. Sci Rep. 2025;15(1):9042. https://doi.org/10.1038/s41598-025-94432-0
  72. 72. Psomiadis E, Philippopoulos PI, Kakaletris G. Non-invasive estimation of crop water stress index and irrigation management with upscaling from field to regional level using remote sensing and agrometeorological data. Remote Sens. 2025;17(14):2522. https://doi.org/10.3390/rs17142522
  73. 73. D’Urso G, Bolognesi SF, Kustas WP, Knipper KR anderson MC, Alsina MM, et al. Determining evapotranspiration by using combination equation models with Sentinel-2 data and comparison with thermal-based energy balance in a California irrigated vineyard. Remote Sens. 2021;13(18):3720. https://doi.org/10.3390/rs13183720
  74. 74. Kang Y, Gao F anderson M, Kustas W, Nieto H, Knipper K, et al. Evaluation of satellite leaf area index in California vineyards for improving water use estimation. Irrig Sci. 2022;40(4–5):531–51. https://doi.org/10.1007/s00271-022-00798-8
  75. 75. Umutoni L, Samadi V. Application of machine learning approaches in supporting irrigation decision making: a review. Agric Water Manag. 2024;294:108710. https://doi.org/10.1016/j.agwat.2024.108710
  76. 76. Appiah SA, Li J, Lan Y, Darko RO, Alordzinu KE, Al Aasmi A, et al. Real-time assessment of mandarin crop water stress index. Sensors. 2022;22(11):4018. https://doi.org/10.3390/s22114018
  77. 77. Mali SS, Scobie M, Baillie J, Plant C, Shammi S, Das A. Integrating UAV-based multispectral and thermal infrared imageries with machine learning for predicting water stress in winter wheat. Precis Agric. 2025;26(3):44. https://doi.org/10.1007/s11119-025-10239-z
  78. 78. Jin K, Zhang J, Wang Z, Zhang J, Liu N, Li M, et al. Application of deep learning based on thermal images to identify the water stress in cotton under film-mulched drip irrigation. Agric Water Manag. 2024;299:108901. https://doi.org/10.1016/j.agwat.2024.108901
  79. 79. Kashyap PK, Kumar S, Jaiswal A, Prasad M, Gandomi AH. Towards precision agriculture: IoT-enabled intelligent irrigation systems using deep learning neural network. IEEE Sens J. 2021;21(16):17479–91. https://doi.org/10.1109/JSEN.2021.3069266
  80. 80. Saikai Y, Peake A, Chenu K. Deep reinforcement learning for irrigation scheduling using high-dimensional sensor feedback. PLOS Water. 2023;2(9):e0000169. https://doi.org/10.1371/journal.pwat.0000169
  81. 81. Zhao H, Di L, Guo L, Li L, Zhang C, Yu E, et al. Optimizing irrigation scheduling using deep reinforcement learning. In: 2023 11th International Conference on Agro-Geoinformatics (Agro-Geoinformatics); Wuhan, China. Piscataway (NJ): IEEE; 2023. p. 1–4. https://doi.org/10.1109/Agro-Geoinformatics59224.2023.10233673
  82. 82. Liu J, Chang F, Wang X, Kang M, Lu C, Wang C, et al. Smart irrigation scheduling for crop production using a crop model and improved deep reinforcement learning. Agriculture. 2025;15(24):2569. https://doi.org/10.3390/agriculture15242569
  83. 83. Chaparro JE, Aedo JE, Lombana NB. Physiology-driven irrigation scheduling in Ananas comosus via hybrid machine learning: UAV-based phenotyping of water-related traits coupled with FAO-56 soil water balance. Plants. 2026;15(14):2112. https://doi.org/10.3390/plants15142112
  84. 84. Alibabaei K, Gaspar PD, Lima TM. Crop yield estimation using deep learning based on climate big data and irrigation scheduling. Energies. 2021;14(11):3004. https://doi.org/10.3390/en14113004
  85. 85. Chandel NS, Chakraborty SK, Rajwade YA, Dubey K, Tiwari MK, Jat D. Identifying crop water stress using deep learning models. Neural Comput Appl. 2021;33(10):5353–67. https://doi.org/10.1007/s00521-020-05325-4
  86. 86. Miller T, Mikiciuk G, Durlik I, Mikiciuk M, Łobodzińska A, Śnieg M. The IoT and AI in agriculture: the time is now—a systematic review of smart sensing technologies. Sensors. 2025;25(12):3583. https://doi.org/10.3390/s25123583
  87. 87. Soussi A, Zero E, Sacile R, Trinchero D, Fossa M. Smart sensors and smart data for precision agriculture: a review. Sensors. 2024;24(8):2647. https://doi.org/10.3390/s24082647
  88. 88. Manocha A, Sood SK, Bhatia M. IoT-digital twin-inspired smart irrigation approach for optimal water utilization. Sustain Comput Inform Syst. 2024;41:100947. https://doi.org/10.1016/j.suscom.2023.100947
  89. 89. Alves RG, Maia RF, Lima F. Development of a digital twin for smart farming: irrigation management system for water saving. J Clean Prod. 2023;388:135920. https://doi.org/10.1016/j.jclepro.2023.135920
  90. 90. Escribà-Gelonch M, Liang S, Van Schalkwyk P, Fisk I, Long NVD, Hessel V. Digital twins in agriculture: orchestration and applications. J Agric Food Chem. 2024;72(19):10737–52. https://doi.org/10.1021/acs.jafc.4c01934
  91. 91. Allen RG, Pereira LS, Raes D, Smith M. Crop evapotranspiration: guidelines for computing crop water requirements. FAO Irrigation and Drainage Paper 56. Rome: Food and Agriculture Organization of the United Nations; 1998. Available from: https://www.fao.org/4/x0490e/x0490e00.html
  92. 92. Zadoks JC, Chang TT, Konzak CF. A decimal code for the growth stages of cereals. Weed Res. 1974;14(6):415–21. https://doi.org/10.1111/j.1365-3180.1974.tb01084.x
  93. 93. Meier U, editor. Growth stages of mono- and dicotyledonous plants: BBCH monograph. Quedlinburg: Julius Kühn-Institut; 2018. https://doi.org/10.5073/20180906-074619
  94. 94. McMaster GS, Wilhelm WW. Growing degree-days: one equation, two interpretations. Agric For Meteorol. 1997;87(4):291–300. https://doi.org/10.1016/S0168-1923(97)00027-0
  95. 95. Jönsson P, Eklundh L. TIMESAT-a program for analyzing time-series of satellite sensor data. Comput Geosci. 2004;30(8):833–45. https://doi.org/10.1016/j.cageo.2004.05.006
  96. 96. Longchamps L, Philpot W. Full-season crop phenology monitoring using two-dimensional normalized difference pairs. Remote Sens. 2023;15(23):5565. https://doi.org/10.3390/rs15235565
  97. 97. Perich G, Hund A anderegg J, Roth L, Boer MP, Walter A, et al. Assessment of multi-image unmanned aerial vehicle based high-throughput field phenotyping of canopy temperature. Front Plant Sci. 2020;11:150. https://doi.org/10.3389/fpls.2020.00150
  98. 98. Ahmed Z, Gui D, Murtaza G, Yunfei L, Ali S. An overview of smart irrigation management for improving water productivity under climate change in drylands. Agronomy. 2023;13(8):2113. https://doi.org/10.3390/agronomy13082113
  99. 99. Ajith S, Vijayakumar S, Elakkiya N. Yield prediction, pest and disease diagnosis, soil fertility mapping, precision irrigation scheduling and food quality assessment using machine learning and deep learning algorithms. Discov Food. 2025;5(1):67. https://doi.org/10.1007/s44187-025-00338-1
  100. 100. Lowder SK, Skoet J, Raney T. The number, size and distribution of farms, smallholder farms and family farms worldwide. World Dev. 2016;87:16–29. https://doi.org/10.1016/j.worlddev.2015.10.041

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